Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12
datasets available to search
ShareScore release 0.9.0
Dataset results
12 results for “light diffuseness”
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Diffuse reflectance spectra of coated plates and corresponding plots transformed Kubelka-Munk function versus the energy of light (eV)
<p>The link contains UV-DRS results of TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> layered composites (from commercial nanoparticles) and corresponding bandgap energies</p>
Summary of measured and modeled light curve parameters for diffuse, direct, and intermediate light curves for 14 whole-canopy 1mx1m plots sampled near the shrub LTER sites at Toolik Field Station, Alaska, summer 2012.
14 1m x 1m shrub plots were sampled the summer of 2012 under direct and diffuse light conditions. Light response curves were measured under each light condition for each plot using a Li-Cor 6400 to measure net ecosystem exchange (NEP); these measurements were modelled using a saturatingMichaelis-Menton formula. The best fit parameters for those models are contained here (Pmax, K, RE, Eo, and light compensation point) for each individual NEP light response curve (direct, diffuse, and intermediate light conditions) measured with corresponding NDVI , LAI, diffuse light fraction, and average temperature. Sorting variables and curve ID numbers for each curve match the corresponding data in the flux data file.
Dataset for "High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use"
<p>Dataset used in the paper</p> <p>Retta MA, Van Doorselaer L, Driever SM, Yin X, de Ruijter NCA, Verboven P, Nicolaï BM, Struik PC. High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO<sub>2</sub> diffusion and efficient light use. New Phytol. 2024 Sep 18. doi: 10.1111/nph.20136. PMID: 39294895.</p> <p>Please cite the paper presenting this datase.</p> <h1><strong>Plant Species and Inbred Lines:</strong></h1> <ul> <li><em>Hirschfeldia incana L. (7th generation inbred line 190003 HIN-NIJ-07-B) </em></li> <li><em>Brassica nigra L. (3rd generation inbred line 210093 BNI-DG1-03-B)</em></li> <li><em>Brassica rapa L. (inbred line ‘R-o-18’)</em></li> <li><em>Arabidopsis thaliana (accession Columbia)</em></li> </ul> <h1><strong>Growth Conditions:</strong></h1> <ul> <li><em>Media:</em> Rock-wool blocks (Grodan Plantop, Roermond, Netherlands, 10×10×7.5 cm)</li> <li><em>Fertigation:</em> Nitrogen-rich nutrient solution via automated dripping system.</li> <li><em>Light Conditions:</em> 12 h day/12 h night, light intensity of 200 µmol m-2 s-1 and 1800 µmol m-2 s-1</li> <li><em>Temperature:</em> Day/Night temperatures of 23 °C and 20 °C, respectively.</li> <li><em>Relative Humidity:</em> 70%</li> </ul> <h1><strong>Codes</strong></h1> <p><strong>Species:</strong></p> <ul> <li><em>Hirschfeldia incana L. - H. incana</em></li> <li><em>Brassica nigra L. - B. nigra</em></li> <li><em>Brassica rapa L. - B. rapa</em></li> <li><em>Arabidopsis thaliana - A. thaliana</em></li> </ul> <p><strong>Light conditions:</strong></p> <ul> <li><em>High light - HL</em></li> <li><em>Low light - LL</em></li> </ul> <p><strong>Replicates:</strong></p> <ul> <li><em>Biological replicates were labeled with numbers, e.g. replicate one from high light grown Hirschfeldia incana is referred to as HiHL1</em></li> </ul> <h1><strong>Measurements</strong></h1> <h2><strong>Leaf Gas Exchange and Chlorophyll Fluorescence Measurements (GasExchangeData.zip):</strong></h2> <ul> <li>Four leaves per species per treatment.</li> <li>Conducted using a LI-6800 (LI-COR, Lincoln, NE, USA) on the mid-position of the youngest fully expanded leaf.</li> <li>The resoponse of photosynthesis to irradiance and external CO2 concentrations augumneted with multi-phase flash fluorescence were made</li> </ul> <h2><strong>Optical Properties Measurement </strong>(<strong>Absorbance & chlorophyll.zip):</strong></h2> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li>Leaf transmittance and reflectance measured using a dual channel spectrophotometer (absorptance_reflac_data_355_750.xlsx)</li> <li>Chlorophyll content measured using a spectrophotometer (Chlorophyll.xlsx).</li> </ul> <h2><strong>Stomatal Density and Size Analysis </strong>(<strong>Stomata.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Leaf-side:</em> abaxial and adxial leaf side.</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Stomatal imprints made using clear nail polish, imaged using a light microscope at 20x.</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> .jpg files organised under folders for species e.g. AtHL\R1 T+B.zip contains images ofimprints of top (T) and bottom (B) leaf sides from replicate plant 1 (R1) of A. thaliana grown under high light (AtHL). The images are named as for example, AT_HL_BOTTOM_R1_A_stacked_minimum.jpg, The leters A to E label various imges made from one imprint.</li> </ul> <h2><strong>Light and Electron Microscopy of Leaf Sections (</strong><strong>CellwallChloroplast.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from four different plants.</li> </ul> <p><strong>Sample preparation</strong></p> <ul> <li>Leaf samples fixed, dehydrated, embedded in Araldite, and sectioned for imaging.</li> <li>1 µm think sections were made for light microscopy</li> <li>Sections of 70 nm were double stained for TEM</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Mesophyll cells imaged at 400x and 700x to measure chloroplast coverage.</li> <li>Electron microscopy performed with Zeiss EM900 electron microscope.</li> </ul> <h2><strong>Mesophyll Chlorophyll (ConfocalData.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Three leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Thickness:</em> 200 ± 10 µm sections prepared using a sliding microtome</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li><em>Microscope:</em> Leica DM8 inverted scope equipped with a Stellaris 5 confocal microscope (Leica Microsystems, Wetzlar, Germany).</li> <li><em>Excitation:</em> 490 nm excitation laser line</li> <li><em>Fluorescence Recording:</em> Chlorophyll autofluorescence recorded in a spectral range of 660−700 nm.</li> <li><em>Objective:</em> Leica objective ×10/0.4 NA.</li> <li><em>Z-Stacks:</em> 85–112 µm depth, two random positions per sample</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> Z-stacks of chlorophyll autofluorescence in mesophyll cells.</li> <li><em>Spectral Information:</em> Chlorophyll autofluorescence recorded in the 660−700 nm range.</li> </ul> <p><strong>Analysis:</strong></p> <ul> <li><em>Software:</em> The confocal files are in .lif format and can be viewed using Leica application suite (LASx), ImageJ</li> </ul>
Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration
<p>This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.</p>
Galactic Diffuse Emission Spectrum between 0.5 and 8.0 MeV and between 30 keV and 8 MeV for the search of PBH and Light Dark Matter
<p>Spectra and response files of the soft gamma-ray spectrum from Inverse Compton scattering in the Milky Way between 0.5 and 8.0 MeV, measured with INTEGRAL/SPI. The fluxes have been extracted with spimodfit assuming GALPROP v56 models according to Voyager 1, AMS-02, and Fermi/LAT data, plus different variants to change the interstellar radiation field, the diffusion properties, or the cosmic-ray halo size (<a href="https://ui.adsabs.harvard.edu/abs/2022A%26A...660A.130S/abstract">Siegert et al., 2022</a>). The respective galdef files are included. In particular:</p> <ul> <li>voyager: propagation model of Bisschoff et al. (2019), Table 2</li> <li>voyager_Dg10001: <span class="math-tex">\(\delta_1 = 0\)</span></li> <li>voyager_Dg1Dg205: <span class="math-tex">\(\delta_1 = \delta_2 = 0.5\)</span></li> <li>voyager_ISRFopt10: factor 10 stronger optical ISFR</li> <li>voyager_z8kpc_KL: thick halo with 8 kpc half-thickness</li> </ul> <p>In addition, there are spectra from 30 keV to 8 MeV (spec_0030-8000keV_<name>.fits, <a href="https://ui.adsabs.harvard.edu/abs/2022PhRvD.106b3030B/abstract">Berteaud et al., 2022</a>), separated into:</p> <ul> <li>CV: spectrum of unresolved point sources in the Galactic plane, mostly cataclysmic variables (strong systematics)</li> <li>IC: spectrum of Inverse Compton scattering of cosmic ray GeV electrons (see above)</li> <li>positronium: spectrum of positronium decay (annihilation of electrons with positrons after intermediate bound state)</li> <li>NFW: spectrum of dark matter contributions that would follow a Navarro Frenk White density profile (mostly consistent with zero)</li> <li>total: spectrum of the above combined</li> </ul> <p>The notebooks for dark matter related bounds when using this data as well as auxiliary files to create the templates are given as (<a href="https://ui.adsabs.harvard.edu/abs/2022PhRvD.106b3030B/abstract">Berteaud et al., 2022</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.4167C/abstract">Calore et al., 2023</a>):</p> <ul> <li>DM_fsr.ipynb: Notebook for final state radiation bounds</li> <li>DM_fsr.py: astromodels function for final state radiation spectrum</li> <li>DM_2gamma.ipynb: Notebook for gamma gamma final state bounds</li> <li>DM_2gamma.py: astromodels function for gamma gamma spectrum</li> <li>Positronium_Spectrum.py: astromodels function for Positonium spectrum</li> <li>DM_pbh.py: script for obtaining PBH bounds</li> <li>diff_@@.csv: spectra for PBH evaporation</li> <li>total_spectrum.fits.gz: total Galactic spectrum</li> <li>NFW_spectrum.fits.gz: NFW-only spectrum</li> </ul>
Transition from Light Diffusion to Localization in Three-Dimensional Amorphous Dielectric Networks near the Band Edge
<p>Data files and codes to generate all data considered in "Transition from Light Diffusion to Localization in Three-Dimensional Amorphous Dielectric Networks near the Band Edge", J. Haberko, L.S. Froufe-Perez, and F. Scheffold, Nature Communications 2020. DOI: 10.1038/s41467-020-18571-w</p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion</em>”</strong> in Optics Express (doi.org/10.1364/OE.521702)<em>. </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage.</p> <p>We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>10050, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>CS50-28, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Mix, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated mixed sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Dark, 10 us.mat</p> </td> <td> <p>Background interference intensity from a custom setup.</p> </td> <td> <p>Na=2048, Nb=5, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Concentrated 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated mixed sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute mixed sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data for the Thorlabs OCT measurements.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis functions.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Custom_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the custom setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_dilute.py</p> </td> <td> <p>The script is for running analysis of raw dilute measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
UV light-generated superhydrophilicity of a titanium surface enhances the transfer, diffusion, and adsorption of osteogenic factors from a collagen sponge
<p>Blood in the collagen sponge does not transfer to untreated implant. While, blood in the collagen sponge immediately and rapidly transfers to UV-treated implant. Blood spreads entire implant body in 10 seconds.</p>
Data for "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble"
<p>The files uploaded here include the data shown in Figures 3.c), 4.a) and 4.b) of the article "In-situ Measurements of Light Diffusion in an Optically Dense Atomic Ensemble", that can be found in: arXiv:2409.11117 </p> <p>Four datasets are included: </p> <p>df_diffusion.csv --> Figure 3.c</p> <p>df_vtransport.csv --> Figure 4.a </p> <p>df_ttransport.csv --> Inset figure 4.a </p> <p>df_decay.csv --> Figure 4.b</p> <p> </p>
Transcranial Near-Infrared Light in Healthy Subjects: a Cerebral Blood Flow Study With Diffuse Correlation Spectroscopy
ClinicalTrials.gov study NCT03740152. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.